Fast and Accurate Non-negative Latent Factor Analysis on High-dimensional and Sparse Matrices in Recommender Systems

Xin Luo, Yue Zhou, Zhigang Liu, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS) matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and Momentum-incorporated Update (SLF-NM2U) algorithm, which enables its fast convergence. It is crucial to achieve a rigorously theoretical proof regarding its fast convergence, which has not been provided in prior research. Aiming at addressing this critical issue, this work theoretically proves that with an appropriately chosen momentum coefficient, SLF-NM2U enables the fast convergence of an FNLF model in both continuous and discrete time cases. Empirical analysis of HiDS matrices generated by representative industrial applications provides empirical evidences for the theoretical proof. Hence, this study represents an important milestone in the field of HiDS matrix analysis.

Original languageEnglish (US)
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Keywords

  • Adaptation models
  • Computational Intelligence
  • Computational modeling
  • Convergence
  • Convergence
  • Data models
  • Data Science
  • High-Dimensional and Sparse Matrix
  • Latent Factor Analysis
  • Momentum
  • Recommender System
  • Recommender systems
  • Single Latent Factor-dependent Non-negative and Multiplicative Update
  • Sparse matrices
  • Standards

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